SCUT Sampling and Classification Algorithms to Identify Levels of Child Malnutrition

Juan Baraybar-Huambo, Juan Gutiérrez-Cárdenas

Resultado de la investigación: Capítulo del libro/informe/acta de congresoArticulo (Contribución a conferencia)

Resumen

Child malnutrition results in millions of deaths every year. This condition is a potential problem in Peruvian society, especially in the rural parts of the country. The consequences of malnutrition range from physical limitations to declining mental performance and productivity for the individual. Government initiatives contribute to decreasing the causes of this disorder; however, these efforts are focused on long term solutions. The need for a fast and reliable way to detect these cases early on still exists. This paper compares classification techniques to determine which one is the most appropriate to classify cases of malnutrition. Neural networks and decision trees are used in combination with different sampling techniques, such as SCUT, SMOTE, random oversampling, random undersampling, and Tomek links. The models produced using oversampling techniques achieved high accuracies. Further, the models produced by the SCUT algorithm achieved high accuracies, preserved the behavior of the data and allowed for better representations of minority classes. The multilayer perceptron model that used the SCUT sampling techniques was chosen as the best model.

Idioma originalInglés
Título de la publicación alojadaInformation Management and Big Data - 6th International Conference, SIMBig 2019, Proceedings
EditoresJuan Antonio Lossio-Ventura, Nelly Condori-Fernandez, Jorge Carlos Valverde-Rebaza
EditorialSpringer
Páginas194-206
Número de páginas13
ISBN (versión impresa)9783030461393
DOI
EstadoPublicada - 1 ene 2020
Evento6th International Conference on Information Management and Big Data, SIMBig 2019 - Lima, Perú
Duración: 21 ago 201923 ago 2019

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1070 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937

Conferencia

Conferencia6th International Conference on Information Management and Big Data, SIMBig 2019
PaísPerú
CiudadLima
Período21/08/1923/08/19

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  • Citar esto

    Baraybar-Huambo, J., & Gutiérrez-Cárdenas, J. (2020). SCUT Sampling and Classification Algorithms to Identify Levels of Child Malnutrition. En J. A. Lossio-Ventura, N. Condori-Fernandez, & J. C. Valverde-Rebaza (Eds.), Information Management and Big Data - 6th International Conference, SIMBig 2019, Proceedings (pp. 194-206). (Communications in Computer and Information Science; Vol. 1070 CCIS). Springer. https://doi.org/10.1007/978-3-030-46140-9_19